The goal of automatic speech recognition (ASR) systems is to determine the lexical identity of spoken utterances. The recognition process, also referred to as classification, begins with the conversion of an input speech signal into a stream of spectral vectors or frames that describe the important characteristics of the signal at specified times. Classification is attempted by first creating reference acoustic models that describe some aspect of the behavior of spectral frames corresponding to different words.
A wide variety of acoustic models have been developed, which typically describe the temporal characteristics of spectra typical to particular words or sub-word segments. The sequence of spectra arising from an input utterance is compared to such acoustic models, and the success with which different acoustic models predict the behavior of the input frames determines the putative identity of the utterance.
Many current systems use some variant of statistical acoustic model such as the hidden Markov model (HMM). Such models consist of sequences of states connected by arcs, and a probability density function (pdf) associated with each state which describes the likelihood of observing any given spectral vector at that state. A separate set of probabilities determines transitions between states. The probability densities which describe the observed spectra associated with the states of the HMM are typically in the form of a continuous pdf which are parametric functions that specify the probability of any arbitrary input spectral vector, given a state. One common class of functions used for this purpose is a mixture of Gaussians where arbitrary pdfs are modeled by a weighted sum of normal distributions. One drawback of using continuous pdfs is that the designer must make explicit assumptions about the nature of the pdf being modeled—something which can be quite difficult since the true distribution form for the speech signal is not known.
The total number of pdfs in a recognition system depends on the number of distinct HMM states, which in turn is determined by type of models used—e.g., phonetic or word models. In many systems the states from different models can be pooled—i.e., the states from different models can share pdfs from a common set or pool. For example, some states from two different models that represent a given phone in different phonetic contexts (i.e., an allophone) may have similar pdfs. In some systems these pdfs will be combined into one, to be shared by both states. This may be done to save memory and in some instances to overcome a problem known as undertraining.
The acoustic model pdfs are most commonly trained as well as adapted to specific conditions using the maximum likelihood method, which adjusts the acoustic model parameters so that the likelihood of observing the training or adaptation data given the model is maximized. But this approach does not necessarily produce optimal recognition performance. Another training approach known as discriminative training adjusts the acoustic model parameters so as to minimize the number of recognition errors rather than fit the distributions to the training data.
FIG. 1 shows a feature vector 10 representative of an input speech frame in a multidimensional vector space, a “correct” state SC 11 from the acoustic model that corresponds to the input speech, and an “incorrect” state SI 12 from an acoustic model that does not correspond to the input speech. As shown in FIG. 1, the vector space distance from the feature vector 10 to the best branch 13 (the closest mixture component) of correct state SC 11, is very nearly the same as the vector space distance from the feature vector 10 to the best branch 14 of the incorrect state SI 12. In this situation, there is very little basis at the state level for distinguishing the correct state SC 11 from the incorrect state SI 12. Discriminative training attempts to adjust the best branch 13 of correct state SC 11 a little closer to the vector space location of feature vector 10, and adjust the best branch 14 of the incorrect state SI 12 a little farther from the vector space location of feature vector 10. Thus, a future feature vector near the vector space of feature vector 10 will be more likely to be identified with correct state SC 11 than with incorrect state SI 12. Of course discriminative training may adjust the vector space of the correct state with respect to multiple incorrect states. Similarly, rather than adjusting the best branches of the states, a set of mixture components within each state may be adjusted.
Discriminative training gives rise to its own problems such as how to appropriately smooth the discriminatively-trained pdfs, and how to adapt these systems to system changes (such as a new user) with a relatively small amount of training data. The foregoing is discussed at more length in U.S. Pat. No. 6,490,555, which is hereby incorporated by reference.
In recent years, literature in the field of speech recognition has described systems which determine a recognition output by combining the output of multiple speech recognition processes for improved overall performance. For convenience, such discussions often are framed in terms of “multiple recognizers.” But such combination approaches need not be limited to the specific situation of entirely different recognition engines and software code. A given speech input may be processed in various different ways which can be thought of as operating in parallel. That is, multiple recognition processes may be implemented on one or more physical machines. The software code used to implement the recognition processes may be entirely different, or part of a single integrated system. Similarly, the acoustic models used may be different in some applications, but in others, multiple recognition processes may share some or all of the acoustic models. Rather, both in the literature and within the present discussion, references to “multiple recognizers” should be understood as referring more broadly to multiple complementary speech recognition processes which operate on a given speech input and produce different competing recognition outputs which are then combined to determine the final recognition output. The output of each of the recognition processes can be in a variety of well-known forms such as a single or multiple sequences of lexical units (words) with the units labeled with confidence estimates or not, or as word graphs or confusion networks, etc. FIG. 2 illustrates the general architecture of a speech recognition system based on multiple recognition processes.
Two popular such approaches are “Recognizer Output Voting for Error Reduction” (ROVER) (for example, described in J. G. Fiscus, A Post-Processing System To Yield Reduced Word Error Rates. Recognizer Output Voting Error Reduction, Proc. ASRU, 1997, hereby incorporated by reference) and “Confusion Network Combination” (CNC) (for example, described in G. Evermann, P. Woodland, Posterior Probability Decoding, Confidence Estimation And System Combination, NIST Workshop, 2000; and L. Mangu, E. Brill, A Stolcke, Finding Consensus In Speech Recognition: Word Error Minimization And Other Applications Of Confusion Networks, CSL, 2000, hereby incorporated by reference).
The success of these methods in terms of improved word error rate (WER) of the combined model pool over each individual performance figure depends on two aspects. On the one hand, each of the combined recognition systems needs to be sufficiently good, and on the other hand, the systems need to make different errors, i.e. they have to be complementary. These two objectives are usually achieved by rather random system variations that ensure only slight performance (WER) differences among systems while they are known to yield at least some complementary system performance. Such variations comprise front-ends, model topology, differently selected or weighted training data and others. Alternatively, systems are combined via ROVER or CNC that have been established by different research groups or model training and decoding software and because of some differences in model and recognizer architecture yield different but similarly good outputs.
A joint training objective for multiple recognition systems for improved performance in combination is described in C. Breslin, M. J. F. Gales, Generating Complementary Systems for Speech Recognition, ICSLP 2006; hereby incorporated by reference. Their approach, motivated by a Minimum Bayes Risk formulation, yields a training data weighting for a second system based on (inverse) posterior probabilities estimated on the reference system. This results in a stronger influence in the parameter estimation of the second system of such utterances that are only poorly modeled by the reference system. Their approach is applied in a Maximum Likelihood framework, but in discriminative training, utterances and words are weighted differently according to posterior probabilities. Breslin and Gales do not indicate how to integrate their approach in a discriminative training framework.